The non-local strategy[1]} has been proved its efficiency on modeling long-distance dependencies of pixel pairs. NLNet [1]} learns a position-independent attention map for each position, which is time-consuming. SENet[3]} uses the global context to calibrate the weights of different channels to adjust the channel dependence, in which the weight calibration inhabits its use of global context. In the paper, a new global context module like GCNet[4]} is introduced to enhance the understanding of the global context in a more efficient way, as shown in Figure REF .
